This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil‐market‐specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil‐market‐specific predictors are optimal. We discuss the policymaking and practical relevance of our results.

中文翻译：

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使用大量经济指标的油价预测模型

本文使用多层感知器人工神经网络研究了布伦特石油期货价格变化的可预测性，该网络利用了尽可能多的经济指标中包含的信息。采用特征工程来识别布伦特石油期货价格变化的最重要预测因素。我们发现石油市场特定变量是重要的预测因素。我们的研究结果还表明，利用所有石油市场特定预测因子的信息内容的多层感知器对布伦特石油期货价格变化的预测表现出比随机游走更高的统计预测准确性。预测最优性测试表明，使用石油市场特定预测因子生成的预测是最优的。我们讨论我们的结果的政策制定和实际相关性。